Programming a computer for playing chess

By Claude E. Shannon. 1950

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In Reinforcement Learning

Citations in the corpus

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017, background): “Babbage, Turing, Shannon, and von Neumann devised hardware, algorithms and theory to analyse and play the game of chess.”
COMPUTER CHESS METHODS (1990, background): “Today, best recognized are the 1965 translation of Adriaan de Groot’s 1946 doctoral dissertation [6] and the much referenced paper on algorithms for playing chess by Claude Shannon [7].”
Genetically programmed strategies for chess endgame (2006, background): “The first article on artificial chess engines was written by Shannon in 1950 [17].”
Deep Blue system overview (1995, background): “Claude Shannon, at the dawn of the modern computer age, presented his seminal paper [10] on computer chess in 1949 at a convention in New York.”
Symbiotic Evolution of Neural Networks in Sequential Decision Tasks (1997, methodology): “1 Focusing Minimax Search Almost all current game programs rely on the minimax search algorithm (Shannon 1950) to return the best move.”
Depth-Limited Solving for Imperfect-Information Games (2018, background): “In perfect-information games, the value that is substituted at a leaf node of a depth-limited subgame is simply an estimate of the state’s value when all players play an equilibrium [34, 32].”
SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE (1987, methodology): “We may regard the subject of arti cial intelligence as beginning with Turing's article Computing Machinery and Intelligence (Turing 1950) and with Shannon's (1950) discussion of how a machine might be programmed to play chess.”
An Introduction to Deep Reinforcement Learning (2018, unknown): “Board-games have also been used for evaluating artificial intelligence methods for decades (Shannon, 1950; Turing, 1953; Samuel, 1959; Sutton, 1988; Littman, 1994; Schraudolph et al., 1994; Tesauro, 1995; Campbell et al., 2002).”
Reinforcement Learning: An Introduction (1998, methodology): “In describing how a computer could be programmed to play chess, Shannon (1950) suggested using an evaluation function that took into account the long-term advantages and disadvantages of chess positions.”